This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction.
We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."
Este curso faz parte do Programa de cursos integrados Statistics with R
Bayesian Statistics
oferecido por
Informações sobre o curso
3.9
469 classificações
Habilidades que você terá
Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming
Programa - O que você aprenderá com este curso
Semana
1About the Specialization and the Course
This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Bayesian Statistics. Please take several minutes read this information. Thanks for joining us in this course!
1 vídeo (total de (Total 2 mín.) min), 4 leituras
1 vídeos
4 leituras
About Statistics with R Specialization10min
About Bayesian Statistics10min
Pre-requisite Knowledge10min
Special Thanks2min
The Basics of Bayesian Statistics
<p>Welcome! Over the next several weeks, we will together explore Bayesian statistics. <p>In this module, we will work with conditional probabilities, which is the probability of event B given event A. Conditional probabilities are very important in medical decisions. By the end of the week, you will be able to solve problems using Bayes' rule, and update prior probabilities.</p><p>Please use the learning objectives and practice quiz to help you learn about Bayes' Rule, and apply what you have learned in the lab and on the quiz.
9 vídeos (total de (Total 41 mín.) min), 2 leituras, 3 testes
9 videos
Conditional Probabilities and Bayes' Rule2min
Bayes' Rule and Diagnostic Testing6min
Bayes Updating2min
Bayesian vs. frequentist definitions of probability4min
Inference for a Proportion: Frequentist Approach3min
Inference for a Proportion: Bayesian Approach7min
Effect of Sample Size on the Posterior2min
Frequentist vs. Bayesian Inference9min
2 leituras
Module Learning Objectivess
Week 1 Lab Instructionss
3 exercícios práticos
Week 1 Lab12min
Week 1 Practice Quiz20min
Week 1 Quiz20min
Semana
2Bayesian Inference
In this week, we will discuss the continuous version of Bayes' rule and show you how to use it in a conjugate family, and discuss credible intervals. By the end of this week, you will be able to understand and define the concepts of prior, likelihood, and posterior probability and identify how they relate to one another.
10 vídeos (total de (Total 45 mín.) min), 2 leituras, 3 testes
10 videos
From the Discrete to the Continuous5min
Elicitation6min
Conjugacy4min
Inference on a Binomial Proportion5min
The Gamma-Poisson Conjugate Families6min
The Normal-Normal Conjugate Families3min
Non-Conjugate Priors4min
Credible Intervals3min
Predictive Inference4min
2 leituras
Module Learning Objectivess
Week 2 Lab Instructionss
3 exercícios práticos
Week 2 Lab28min
Week 2 Practice Quiz20min
Week 2 Quiz40min
Semana
3Decision Making
In this module, we will discuss Bayesian decision making, hypothesis testing, and Bayesian testing. By the end of this week, you will be able to make optimal decisions based on Bayesian statistics and compare multiple hypotheses using Bayes Factors.
14 vídeos (total de (Total 75 mín.) min), 2 leituras, 3 testes
14 videos
Losses and decision making3min
Working with loss functions6min
Minimizing expected loss for hypothesis testing5min
Posterior probabilities of hypotheses and Bayes factors6min
The Normal-Gamma Conjugate Family6min
Inference via Monte Carlo Sampling3min
Predictive Distributions and Prior Choice5min
Reference Priors7min
Mixtures of Conjugate Priors and MCMC6min
Hypothesis Testing: Normal Mean with Known Variance7min
Comparing Two Paired Means Using Bayes' Factors6min
Comparing Two Independent Means: Hypothesis Testing3min
Comparing Two Independent Means: What to Report?5min
2 leituras
Module Learning Objectivess
Week 3 Lab Instructionss
3 exercícios práticos
Week 3 Lab22min
Week 3 Practice Quiz16min
Week 3 Quiz40min
Semana
4Bayesian Regression
This week, we will look at Bayesian linear regressions and model averaging, which allows you to make inferences and predictions using several models. By the end of this week, you will be able to implement Bayesian model averaging, interpret Bayesian multiple linear regression and understand its relationship to the frequentist linear regression approach.
11 vídeos (total de (Total 72 mín.) min), 2 leituras, 3 testes
11 videos
Bayesian simple linear regression8min
Checking for outliers4min
Bayesian multiple regression4min
Model selection criteria5min
Bayesian model uncertainty7min
Bayesian model averaging7min
Stochastic exploration8min
Priors for Bayesian model uncertainty8min
R demo: crime and punishment9min
Decisions under model uncertainty7min
2 leituras
Module Learning Objectivess
Week 4 Lab Instructionss
3 exercícios práticos
Week 4 Lab22min
Week 4 Practice Quiz20min
Week 4 Quiz40min
17%
comecei uma nova carreira após concluir estes cursos
14%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
por RR•Sep 21st 2017
Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.
por GH•Apr 10th 2018
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
Instrutores
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
David Banks
Professor of the PracticeStatistical Science
Colin Rundel
Assistant Professor of the PracticeStatistical Science
Merlise A Clyde
ProfessorDepartment of Statistical Science
Sobre Duke University
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Sobre o Programa de cursos integrados Statistics with R
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
What background knowledge is necessary?
We assume you have knowledge equivalent to the prior courses in this specialization.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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